Efficient Key Generation on Lattice Cryptography for Privacy Protection in Mobile IoT Crowdsourcing

作者：Hai Lu, Yan Zhu, E Chen, Rongquan Feng, Lejun Zhang, Di Ma · 发表于：IEEE Internet of Things Journal · 年份：2023 · DOI：10.1109/jiot.2023.3328719 · 被引用次数：4 · 研究领域：Cryptography and Data Security、Privacy-Preserving Technologies in Data、Advanced Steganography and Watermarking Techniques

To face urgent concern of privacy leakage on mobile crowdsourcing, some Lattice-Based Cryptographic (LBC) schemes have been applied to the cloud-fog-edge data sharing platform for privacy protection. As an important factor of LBC’s security, current key generation usually involves Preimage Gaussian Sampling for Lattice Trapdoor (PGS-LT) to sample short preimage vector from dual lattice. However, there lacks researches on the implementation of PGS-LT according to entities’ computation and storage capacities. To address this issue, we present a fast double-perturbation scheme that is applied to the cloud-fog-edge data sharing platform. Firstly, we design a fast spherical G-lattice sampling algorithm including two samplers: G-perturbation sampler and G-lattice sampler. Among them, the fast non-spherical G-lattice sampling algorithm is extended to arbitrary bases, and deployed on the G-lattice sampler. Meanwhile, the G-perturbation sampler is designed to sample G-perturbation for converting the non-spherical distribution of output G-lattice vector to the spherical one. Secondly, we optimize the assignment of computational tasks in PGS-LT by considering entities’ abilities in the cloud-fog-edge platform. Moreover, we analyze three types of delegated preimage sampling in terms of Gaussian quality and complexity. The analysis and experimental results show that fast spherical G-lattice sampling provides high Gaussian quality of output vector. Meanwhile, in the aspect of complexity, t...